Multilevel Proteomic Profiling of Colorectal Adenocarcinoma Caco-2 Cell Differentiation to Characterize an Intestinal Epithelial Model
Bibliographic record
Abstract
Emergent advancements on the role of the intestinal microbiome for human health and disease necessitate well-defined intestinal cellular models to study and rapidly assess host, microbiome, and drug interactions. Differentiated Caco-2 cell line is commonly utilized as an epithelial model for drug permeability studies and has more recently been utilized for investigating host-microbiome interactions. However, its suitability to study such interactions remains to be characterized. Here, we employed multilevel proteomics to demonstrate that both spontaneous and butyrate-induced Caco-2 differentiations displayed similar protein and pathway changes, including the downregulation of proteins related to translation and proliferation and upregulation of functions implicated in host-microbiome interactions, such as cell adhesion, tight junction, extracellular vesicles, and responses to stimuli. Lysine acetylomics revealed that histone protein acetylation levels were decreased along with cell differentiation, while the acetylation in proteins associated with mitochondrial functions was increased. This study also demonstrates that, compared to spontaneous differentiation methods, butyrate-containing medium accelerates Caco-2 differentiation, with earlier upregulation of proteins related to host-microbiome interactions, suggesting its superiority for assay development using this intestinal model. Altogether, this multiomics study emphasizes the controlled progression of Caco-2 differentiation toward a specialized intestinal epithelial-like cell and establishes its suitability for investigating the host-microbiome interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".